Evidence map›Paper›PMID 38977339›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024

[Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database].

L Chen, T Wu, M Zhang, Z Ding, Y Zhang, Y Yang, J Zheng, X Zhang

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Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

L ChenSchool of Health Management, Bengbu Medical University, Bengbu 233030, China.
T WuSchool of Public Health, Bengbu Medical University, Bengbu 233030, China.
M ZhangKey Laboratory of Cardiovascular and Cerebrovascular Diseases, Bengbu Medical University, Bengbu 233030, China.
Z DingCollege of Clinical Medicine, Bengbu Medical University, Bengbu 233030, China.
Y ZhangCollege of Clinical Medicine, Bengbu Medical University, Bengbu 233030, China.
Y YangCollege of Clinical Medicine, Bengbu Medical University, Bengbu 233030, China.
J ZhengCollege of Clinical Medicine, Bengbu Medical University, Bengbu 233030, China.
X ZhangKey Laboratory of Cardiovascular and Cerebrovascular Diseases, Bengbu Medical University, Bengbu 233030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms.

methodsThe differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA).

resultsWe identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (

conclusionCD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Indexed as

Arthritis, RheumatoidBiomarkersProtein Interaction MapsSTAT1 Transcription FactorSynovial MembraneAnimalsArthritis, ExperimentalCD8 AntigensDatabases, GeneticGene Expression ProfilingHumansLymphocyte Specific Protein Tyrosine Kinase p56(lck)RatsReceptors, CCR5ROC CurveSyk KinaseBiomarkersCD8 AntigensLymphocyte Specific Protein Tyrosine Kinase p56(lck)Receptors, CCR5STAT1 Transcription FactorSyk Kinasebiomarkersbionformaticimmune regulatorymachine learningrheumatoid arthritis

Identifiers

PMID38977339
PMCPMC11237296

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